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Unlike LLMs, we can’t scrape the internet for robot data Announcing Project Go-Big: we’re building the world's largest humanoid pretraining dataset This is accelerated by our partnership with Brookfield, who owns over 100,000 residential units

Figure

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616,555 views • 10 months ago •via X (Twitter)

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Figure is aiming to develop the world’s largest and most diverse real-world humanoid pretraining dataset. For this purpose, they’re partnering with Brookfield, a global asset manager overseeing $1 trillion in assets, including 100,000 residential units, 500M square feet of commercial office space, and 160M square feet of logistics space. The data collected from this collaboration will be used to train Figure’s Helix AI model, enabling humanoids to perform tasks autonomously in real-world environments designed for humans. In addition to data collection, the partnership will explore support for next-generation GPU data centers, real estate for robotic training environments, and commercial use cases across Brookfield’s global footprint.

Figure is aiming to develop the world’s largest and most diverse real-world humanoid pretraining dataset. For this purpose, they’re partnering with Brookfield, a global asset manager overseeing $1 trillion in assets, including 100,000 residential units, 500M square feet of commercial office space, and 160M square feet of logistics space. The data collected from this collaboration will be used to train Figure’s Helix AI model, enabling humanoids to perform tasks autonomously in real-world environments designed for humans. In addition to data collection, the partnership will explore support for next-generation GPU data centers, real estate for robotic training environments, and commercial use cases across Brookfield’s global footprint.

The Humanoid Hub

88,600 views • 10 months ago

We’re thrilled to announce our largest payment partnership to date with zerohash. Together, we are building the rails for money movement that will bring the world’s leading companies onto Plasma.

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Honoured to welcome President Droupadi Murmu — the first visit by an Indian head of state to Moldova. India, the world’s largest democracy, is a valued friend. We’re building a partnership that delivers real results for our people.

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Plasma will launch mainnet beta with over $1B in stablecoin TVL. Capital efficiency is the foundation for deep stablecoin liquidity and seamless digital dollar markets. Today we’re announcing our partnership with @0xFluid, the most capital efficient DEX for stablecoins.

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AxonDAO

33,926 views • 1 year ago

It's been incredible to see neural networks working so well on our humanoid robots Humanoids are crazy complex - an individual motor can rotate 360 degrees and you have 40+ joints. If you do the math, that means more possible robot states than atoms in the universe Figure has our own AI model called Helix that we've designed in-house. A single Helix neural network now outputs both manipulation and navigation, end-to-end from language and pixel input Every leap in machine learning has come from massive, diverse datasets. At Figure, we’re currently building the largest pretraining dataset for humanoids in history - excited to see what this unlocks

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93,986 views • 10 months ago

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Today we’re releasing SAIR, the Structurally Augmented IC50 Repository. SAIR is the Largest Open-Sourced Binding Affinity Dataset with Cofolded 3D Structures. It includes more than 5 million protein-ligand structures, generated using our Large Quantitative Models and labeled with binding affinity data. By providing this unprecedented scale of structure-activity data, we aim to enable researchers to train and evaluate new AI models for drug discovery, bridging the historical gap between molecular structures and drug potency prediction. The SAIR dataset was created using the NVIDIA DGX Cloud and is now publicly available on the Google Cloud Platform. Access and build with SAIR today! 📰Read the Press Release: 📥Learn More and Download the Dataset at #DrugDiscovery #LQMs #SAIR #AIforScience #SandboxAQ

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SandboxAQ

129,416 views • 1 year ago

OSINTdefender At Tianjin Winter Gala Festival in China, a Unitree H1 humanoid robot malfunctioned and unexpectedly lunged toward a spectator who had extended their hand for a handshake. This is what our tech elite are racing toward: machines we can’t control, built by regimes we don’t trust.

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50,087 views • 1 year ago

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27,858 views • 7 months ago

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105,106 views • 1 year ago

There is nothing more terrible than war, and we, Ukrainians, those who stayed in our homes, know this for sure, not from the news. Odesa, a strike on a residential building after sunset.

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54,680 views • 1 year ago

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50,995 views • 2 years ago

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The Base Ecosystem Fund, led by Coinbase Ventures 🛡️, is making a strategic investment on Talent Protocol. After a year of building together, Base is doubling down on our partnership. Tomorrow, we take another big step, with $TALENT launching on Base. Still day one, keep building.

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139,464 views • 1 year ago

To replace animal testing with AI, we need MASSIVE human datasets. Today, we're thrilled to share Axiom's new data exploration tool, providing the ability to visually explore the world's largest primary human liver toxicity dataset. Built with Axiom's proprietary wetlab protocols, our dataset includes detailed liver toxicity profiles for over 100,000 distinct molecules. The key to this dataset is our ability to do high-throughput, multiplexed high-content screening with primary human liver cells. Traditionally, toxicity assays either sacrifice throughput or sacrifice biological relevance (using easy-to-grow immortalized cell lines instead of real human cells). We managed to combine throughput, physiological relevance, and multiplexing in one platform. The assays run in a high throughput format using automation, meaning thousands of compound-dose conditions can be tested in one experiment. We achieved this using pooled primary human hepatocytes, which are often fragile and expensive. By systemizing our automation and quality control processes, we were able to run over 120+ batches on the same donor pool with incredible reproducibility and consistency. We did this while integrating many readouts per well, whereas many existing toxicity assays only do a single readout. Our multiplexed approach provides far more data per experiment enabling us to measure 10-20 different toxicity phenotypes such as apoptosis, necrosis, mitochondrial fission, endoplasmic reticulum stress, stress granule formation, microtubules, and more all from a single well on a 384-well plate! The combination of scale, high content information, and data quality is exactly what is needed to train highly accurate AI models in biology. If you're interested, please explore the dataset in the comments below and let me know if you want to chat about the details!

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25,117 views • 1 year ago

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VentureMind AI

22,406 views • 1 year ago

Today may be the ImageNet moment for robotics. RT-X: the largest open-source robot dataset ever compiled, across 33 institutes, 22 robot hardware, 527 skills, and 1M episodes. Why is robotics lagging so far behind NLP, vision, and other AI domains? Data scarcity is the main culprit to blame, among other difficulties. Unlike text, images, and videos, you cannot download mass amounts of onboard robot control data from the internet. They simply don't exist in the wild. 11 yrs ago, ImageNet kicked off the deep learning revolution. 3-4 yrs ago, internet-scale data fueled the first GPTs and Diffusions that define this era of foundation models. I think 2023 is finally the year for robotics to scale up. Robot foundation models like VIMA ( my team's work at NVIDIA) and RT-1/2 ( Google DeepMind's effort) are extremely data hungry. While massively parallel simulations like NVIDIA IsaacGym & Omniverse can alleviate the problem to some extent, it's still not quite enough to bridge the gap to the messy, physical world. This new dataset is not just a technical contribution. I also see it as a commendable effort to overcome institutional bureaucracies and unite researchers from around the world to tackle a grand challenge together. Robotics will be the final holy grail that we capture in AI. We are not there yet, but ascending in the right gradient direction. RT-X website: Launch blog:

Today may be the ImageNet moment for robotics. RT-X: the largest open-source robot dataset ever compiled, across 33 institutes, 22 robot hardware, 527 skills, and 1M episodes. Why is robotics lagging so far behind NLP, vision, and other AI domains? Data scarcity is the main culprit to blame, among other difficulties. Unlike text, images, and videos, you cannot download mass amounts of onboard robot control data from the internet. They simply don't exist in the wild. 11 yrs ago, ImageNet kicked off the deep learning revolution. 3-4 yrs ago, internet-scale data fueled the first GPTs and Diffusions that define this era of foundation models. I think 2023 is finally the year for robotics to scale up. Robot foundation models like VIMA ( my team's work at NVIDIA) and RT-1/2 ( Google DeepMind's effort) are extremely data hungry. While massively parallel simulations like NVIDIA IsaacGym & Omniverse can alleviate the problem to some extent, it's still not quite enough to bridge the gap to the messy, physical world. This new dataset is not just a technical contribution. I also see it as a commendable effort to overcome institutional bureaucracies and unite researchers from around the world to tackle a grand challenge together. Robotics will be the final holy grail that we capture in AI. We are not there yet, but ascending in the right gradient direction. RT-X website: Launch blog:

Jim Fan

265,038 views • 2 years ago

You thought the sky was the limit? Wingbits is going to space 🚀 thanks to a strategic partnership with Spire, leader in space-based data and operator of the largest multipurpose satellite constellation. 🛰️ Together we’re launching a new satellite that will act as a validator of Wingbits’ ground data, increasing the security and reliability of our global flight tracking network. The satellite is scheduled for launch on the SpaceX Transporter-13 mission.

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wingbits

243,961 views • 1 year ago

Today, we’re announcing our $160 million Series D funding, led by Kleiner Perkins, with participation from Sequoia Capital, Thrive Capital, J.P. Morgan, and Khosla Ventures. 35,000+ financial professionals. 250+ institutions. One platform, purpose-built for finance. Thank you to our clients, our team, and our investors for their support as we continue building to raise the ceiling for what finance can accomplish.

Today, we’re announcing our $160 million Series D funding, led by Kleiner Perkins, with participation from Sequoia Capital, Thrive Capital, J.P. Morgan, and Khosla Ventures. 35,000+ financial professionals. 250+ institutions. One platform, purpose-built for finance. Thank you to our clients, our team, and our investors for their support as we continue building to raise the ceiling for what finance can accomplish.

Rogo

124,925 views • 3 months ago